Atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00380181" target="_blank" >RIV/68407700:21230/25:00380181 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-73284-3_13" target="_blank" >https://doi.org/10.1007/978-3-031-73284-3_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-73284-3_13" target="_blank" >10.1007/978-3-031-73284-3_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound Images

Popis výsledku v původním jazyce

We aim to predict the stability of carotid artery plaques from longitudinal ultrasound images. This is important since atherosclerosis is the primary cause of heart disease and stroke. Accurately predicting plaque stability would allow for more targeted follow-up and treatment, saving healthcare costs. We analyze data from over 400 patients followed for 3 years, exceeding the size of previous studies. We first localize the carotid artery and segment the plaque within the images. A self-supervised learning approach was used for plaque segmentation, leveraging the power of unlabeled data. The plaque stability predictor uses three image channels derived from the ultrasound image and its segmentation. As an auxiliary task, we predict the plaque width, which helps to prevent overfitting. The balance between the criteria is maintained automatically. Our estimate of the plaque width correlated well with expert measurements. We confirmed that there is a relationship between the plaque ultrasound appearance in longitudinal images and their stability. However, the future width correlation and the plaque stability prediction performance remained modest (AUC = 0.61), similar to previous studies.

Název v anglickém jazyce

Atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound Images

Popis výsledku anglicky

We aim to predict the stability of carotid artery plaques from longitudinal ultrasound images. This is important since atherosclerosis is the primary cause of heart disease and stroke. Accurately predicting plaque stability would allow for more targeted follow-up and treatment, saving healthcare costs. We analyze data from over 400 patients followed for 3 years, exceeding the size of previous studies. We first localize the carotid artery and segment the plaque within the images. A self-supervised learning approach was used for plaque segmentation, leveraging the power of unlabeled data. The plaque stability predictor uses three image channels derived from the ultrasound image and its segmentation. As an auxiliary task, we predict the plaque width, which helps to prevent overfitting. The balance between the criteria is maintained automatically. Our estimate of the plaque width correlated well with expert measurements. We confirmed that there is a relationship between the plaque ultrasound appearance in longitudinal images and their stability. However, the future width correlation and the plaque stability prediction performance remained modest (AUC = 0.61), similar to previous studies.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/NV19-08-00362" target="_blank" >NV19-08-00362: Hodnocení stability aterosklerotického plátu v karotidě pomocí digitální analýzy ultrazvukového obrazu u pacientů se stenózou vnitřní karotidy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

MLMI 2024: MICCAI 2024 Workshop on Machine Learning in Medical Imaging, Part I

ISBN

978-3-031-73283-6

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

9

Strana od-do

124-132

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Místo konání akce

MARRAKESH

Datum konání akce

6. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001424557900013